Kalman Filter

[catsally1]

I'm looking for someone that feels the have developed a good working Kalman Filter Model to use in the fx market?

[[Deleted]]

catsally1: I'm looking for someone that feels the have developed a good working Kalman Filter Model to use in the fx market?

How about letting your fingers do the walking with the "Search" facility: https://www.mql5.com/en/search#!keyword=Kalman%20Filter

[Sergey Golubev]

• Non-Linear Kalman Filter indicator - the post 
• KalmanBands indicator - the post 
• Kalman filter - indicator for MetaTrader 4
• Nonlinear Kalman filter - indicator for MetaTrader 5
• Kalman_Filter - indicator for MetaTrader 5

[Sergey Golubev]

Forum on trading, automated trading systems and testing trading strategies

Something Interesting

Sergey Golubev, 2025.02.27 16:49

The Kalman Filter for Forex Mean-Reversion Strategies

The Kalman filter is a recursive algorithm used in algorithmic trading to estimate the true state of a financial time series by filtering out noise from price movements. It dynamically updates predictions based on new market data, making it valuable for adaptive strategies like mean reversion. This article first introduces the Kalman filter, covering its calculation and implementation. Next, we apply the filter to a classic mean-reversion forex strategy as an example. Finally, we conduct various statistical analyses by comparing the filter with a moving average across different forex pairs.

[Sergey Golubev]

A Practical Kalman Filter Price Smoother in MQL5: Adaptive Noise Estimation Without External Libraries

This article presents that practical solution: a scalar state‑space Kalman smoother whose blending weight is the optimally computed Kalman Gain and whose process and measurement noise variances are estimated online from rolling windows of returns and price deviations.

[Sergey Golubev]

The Kalman Filter for Price Smoothing: An Exact Implementation in MQL5

The Kalman Filter, developed by Rudolf Kalman in 1960 and famously used for spacecraft navigation on the Apollo program, approaches this differently. Rather than a fixed weighting scheme, it maintains an explicit estimate of its own uncertainty at every step, and uses that uncertainty to calculate, mathematically, exactly how much weight a new observation deserves. When the filter is confident in its current estimate, it leans on that. When a new observation is more informative than what came before, it leans on that instead. This weighting — the Kalman gain — is not a value entered directly; it is computed deterministically from the noise parameters Q and R that are chosen by hand, and it converges toward a steady-state value set by their ratio. The gain formula itself involves no manual tuning, but the Q and R inputs that drive it are still a user choice.